Documentation Index
Fetch the complete documentation index at: https://docs.agno.com/llms.txt
Use this file to discover all available pages before exploring further.
Setup
docker run --rm \
--pull always \
-p 8000:8000 \
surrealdb/surrealdb:latest \
start \
--user root \
--pass root
./cookbook/scripts/run_surrealdb.sh
Example
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.surrealdb import SurrealDb
from surrealdb import Surreal
# SurrealDB connection parameters
SURREALDB_URL = "ws://localhost:8000"
SURREALDB_USER = "root"
SURREALDB_PASSWORD = "root"
SURREALDB_NAMESPACE = "test"
SURREALDB_DATABASE = "test"
# Create a client
client = Surreal(url=SURREALDB_URL)
client.signin({"username": SURREALDB_USER, "password": SURREALDB_PASSWORD})
client.use(namespace=SURREALDB_NAMESPACE, database=SURREALDB_DATABASE)
surrealdb = SurrealDb(
client=client,
collection="recipes", # Collection name for storing documents
efc=150, # HNSW construction time/accuracy trade-off
m=12, # HNSW max number of connections per element
search_ef=40, # HNSW search time/accuracy trade-off
)
def sync_demo():
"""Demonstrate synchronous usage of SurrealDb"""
knowledge_base = Knowledge(
vector_db=surrealdb,
embedder=OpenAIEmbedder(),
)
# Load data synchronously
knowledge_base.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
# Create agent and query synchronously
agent = Agent(knowledge=knowledge_base)
agent.print_response(
"What are the 3 categories of Thai SELECT is given to restaurants overseas?",
markdown=True,
)
if __name__ == "__main__":
# Run synchronous demo
print("Running synchronous demo...")
sync_demo()
Async Support ⚡
SurrealDB also supports asynchronous operations, enabling concurrency and leading to better performance.
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.surrealdb import SurrealDb
from surrealdb import AsyncSurreal
# SurrealDB connection parameters
SURREALDB_URL = "ws://localhost:8000"
SURREALDB_USER = "root"
SURREALDB_PASSWORD = "root"
SURREALDB_NAMESPACE = "test"
SURREALDB_DATABASE = "test"
# Create a client
client = AsyncSurreal(url=SURREALDB_URL)
surrealdb = SurrealDb(
async_client=client,
collection="recipes", # Collection name for storing documents
efc=150, # HNSW construction time/accuracy trade-off
m=12, # HNSW max number of connections per element
search_ef=40, # HNSW search time/accuracy trade-off
)
async def async_demo():
"""Demonstrate asynchronous usage of SurrealDb"""
await client.signin({"username": SURREALDB_USER, "password": SURREALDB_PASSWORD})
await client.use(namespace=SURREALDB_NAMESPACE, database=SURREALDB_DATABASE)
knowledge_base = Knowledge(
vector_db=surrealdb,
embedder=OpenAIEmbedder(),
)
await knowledge_base.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent = Agent(knowledge=knowledge_base)
await agent.aprint_response(
"What are the 3 categories of Thai SELECT is given to restaurants overseas?",
markdown=True,
)
if __name__ == "__main__":
# Run asynchronous demo
print("\nRunning asynchronous demo...")
asyncio.run(async_demo())
Using aload() and aprint_response() with asyncio provides non-blocking operations, making your application more responsive under load.
SurrealDB Params
| Parameter | Type | Default | Description |
|---|
client | Optional[Union[BlockingWsSurrealConnection, BlockingHttpSurrealConnection]] | None | A blocking connection, either HTTP or WS |
async_client | Optional[Union[AsyncWsSurrealConnection, AsyncHttpSurrealConnection]] | None | An async connection, either HTTP or WS |
collection | str | "documents" | Collection name to store documents |
distance | Distance | Distance.cosine | Distance metric to use (cosine, l2, or max_inner_product) |
efc | int | 150 | HNSW construction time/accuracy trade-off |
m | int | 12 | HNSW max number of connections per element |
search_ef | int | 40 | HNSW search time/accuracy trade-off |
embedder | Optional[Embedder] | OpenAIEmbedder() | Embedder instance for creating embeddings |
